{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stockmarket analysis with `pmdarima`\n",
    "\n",
    "This example follows the post on [Towards Data Science](https://towardsdatascience.com/stock-market-analysis-using-arima-8731ded2447a) (TDS), demonstrating the use of `pmdarima` to simplify time series analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using pmdarima 1.3.0-dev0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import pmdarima as pm\n",
    "print(f\"Using pmdarima {pm.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the data\n",
    "\n",
    "`pmdarima` contains an embedded `datasets` submodule that allows us to try out models on common datasets. We can load the MSFT stock data from `pmdarima` 1.3.0+:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>OpenInt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986-03-13</td>\n",
       "      <td>0.06720</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>0.06720</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>1371330506</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986-03-14</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>409569463</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1986-03-17</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>176995245</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1986-03-18</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>90067008</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1986-03-19</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>0.07533</td>\n",
       "      <td>63655515</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date     Open     High      Low    Close      Volume  OpenInt\n",
       "0  1986-03-13  0.06720  0.07533  0.06720  0.07533  1371330506        0\n",
       "1  1986-03-14  0.07533  0.07533  0.07533  0.07533   409569463        0\n",
       "2  1986-03-17  0.07533  0.07533  0.07533  0.07533   176995245        0\n",
       "3  1986-03-18  0.07533  0.07533  0.07533  0.07533    90067008        0\n",
       "4  1986-03-19  0.07533  0.07533  0.07533  0.07533    63655515        0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pmdarima.datasets.stocks import load_msft\n",
    "\n",
    "df = load_msft()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data\n",
    "\n",
    "As in the blog post, we'll use 80% of the samples as training data. Note that a time series' train/test split is different from that of a dataset without temporality; order *must* be preserved if we hope to discover any notable trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6386 train samples\n",
      "1597 test samples\n"
     ]
    }
   ],
   "source": [
    "train_len = int(df.shape[0] * 0.8)\n",
    "train_data, test_data = df[:train_len], df[train_len:]\n",
    "\n",
    "y_train = train_data['Open'].values\n",
    "y_test = test_data['Open'].values\n",
    "\n",
    "print(f\"{train_len} train samples\")\n",
    "print(f\"{df.shape[0] - train_len} test samples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-modeling analysis\n",
    "\n",
    "TDS fixed ``p`` at 5 based on some lag plot analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1152 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import lag_plot\n",
    "\n",
    "fig, axes = plt.subplots(3, 2, figsize=(12, 16))\n",
    "plt.title('MSFT Autocorrelation plot')\n",
    "\n",
    "# The axis coordinates for the plots\n",
    "ax_idcs = [\n",
    "    (0, 0),\n",
    "    (0, 1),\n",
    "    (1, 0),\n",
    "    (1, 1),\n",
    "    (2, 0),\n",
    "    (2, 1)\n",
    "]\n",
    "\n",
    "for lag, ax_coords in enumerate(ax_idcs, 1):\n",
    "    ax_row, ax_col = ax_coords\n",
    "    axis = axes[ax_row][ax_col]\n",
    "    lag_plot(df['Open'], lag=lag, ax=axis)\n",
    "    axis.set_title(f\"Lag={lag}\")\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All lags look fairly linear, so it's a good indicator that an auto-regressive model is a good choice. Therefore, we'll allow the `auto_arima` to select the lag term for us, up to 6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimating the differencing term"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can estimate the best lag term with several statistical tests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated differencing term: 1\n"
     ]
    }
   ],
   "source": [
    "from pmdarima.arima import ndiffs\n",
    "\n",
    "kpss_diffs = ndiffs(y_train, alpha=0.05, test='kpss', max_d=6)\n",
    "adf_diffs = ndiffs(y_train, alpha=0.05, test='adf', max_d=6)\n",
    "n_diffs = max(adf_diffs, kpss_diffs)\n",
    "\n",
    "print(f\"Estimated differencing term: {n_diffs}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `auto_arima` to fit a model on the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit ARIMA: order=(2, 1, 2); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(0, 1, 0); AIC=5640.878, BIC=5654.402, Fit time=0.004 seconds\n",
      "Fit ARIMA: order=(1, 1, 0); AIC=5640.426, BIC=5660.711, Fit time=0.074 seconds\n",
      "Fit ARIMA: order=(0, 1, 1); AIC=5640.350, BIC=5660.635, Fit time=0.050 seconds\n",
      "Fit ARIMA: order=(1, 1, 1); AIC=5640.152, BIC=5667.198, Fit time=0.607 seconds\n",
      "Fit ARIMA: order=(1, 1, 2); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(2, 1, 1); AIC=5641.440, BIC=5675.249, Fit time=0.556 seconds\n",
      "Total fit time: 2.311 seconds\n"
     ]
    }
   ],
   "source": [
    "auto = pm.auto_arima(y_train, d=n_diffs, seasonal=False, stepwise=True,\n",
    "                     suppress_warnings=True, error_action=\"ignore\", max_p=6,\n",
    "                     max_order=None, trace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "print(auto.order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.3416473178248818\n",
      "SMAPE: 0.981464018635346\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from pmdarima.metrics import smape\n",
    "\n",
    "model = auto\n",
    "\n",
    "def forecast_one_step():\n",
    "    fc, conf_int = model.predict(n_periods=1, return_conf_int=True)\n",
    "    return (\n",
    "        fc.tolist()[0],\n",
    "        np.asarray(conf_int).tolist()[0])\n",
    "\n",
    "forecasts = []\n",
    "confidence_intervals = []\n",
    "\n",
    "for new_ob in y_test:\n",
    "    fc, conf = forecast_one_step()\n",
    "    forecasts.append(fc)\n",
    "    confidence_intervals.append(conf)\n",
    "    \n",
    "    # Updates the existing model with a small number of MLE steps\n",
    "    model.update(new_ob)\n",
    "    \n",
    "print(f\"Mean squared error: {mean_squared_error(y_test, forecasts)}\")\n",
    "print(f\"SMAPE: {smape(y_test, forecasts)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12532d080>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(12, 12))\n",
    "\n",
    "# --------------------- Actual vs. Predicted --------------------------\n",
    "axes[0].plot(y_train, color='blue', label='Training Data')\n",
    "axes[0].plot(test_data.index, forecasts, color='green', marker='o',\n",
    "             label='Predicted Price')\n",
    "\n",
    "axes[0].plot(test_data.index, y_test, color='red', label='Actual Price')\n",
    "axes[0].set_title('Microsoft Prices Prediction')\n",
    "axes[0].set_xlabel('Dates')\n",
    "axes[0].set_ylabel('Prices')\n",
    "\n",
    "axes[0].set_xticks(np.arange(0, 7982, 1300).tolist(), df['Date'][0:7982:1300].tolist())\n",
    "axes[0].legend()\n",
    "\n",
    "\n",
    "# ------------------ Predicted with confidence intervals ----------------\n",
    "axes[1].plot(y_train, color='blue', label='Training Data')\n",
    "axes[1].plot(test_data.index, forecasts, color='green',\n",
    "             label='Predicted Price')\n",
    "\n",
    "axes[1].set_title('Prices Predictions & Confidence Intervals')\n",
    "axes[1].set_xlabel('Dates')\n",
    "axes[1].set_ylabel('Prices')\n",
    "\n",
    "conf_int = np.asarray(confidence_intervals)\n",
    "axes[1].fill_between(test_data.index,\n",
    "                     conf_int[:, 0], conf_int[:, 1],\n",
    "                     alpha=0.9, color='orange',\n",
    "                     label=\"Confidence Intervals\")\n",
    "\n",
    "axes[1].set_xticks(np.arange(0, 7982, 1300).tolist(), df['Date'][0:7982:1300].tolist())\n",
    "axes[1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1986-03-13\n",
       "1       1986-03-14\n",
       "2       1986-03-17\n",
       "3       1986-03-18\n",
       "4       1986-03-19\n",
       "5       1986-03-20\n",
       "6       1986-03-21\n",
       "7       1986-03-24\n",
       "8       1986-03-25\n",
       "9       1986-03-26\n",
       "10      1986-03-27\n",
       "11      1986-03-31\n",
       "12      1986-04-01\n",
       "13      1986-04-02\n",
       "14      1986-04-03\n",
       "15      1986-04-04\n",
       "16      1986-04-07\n",
       "17      1986-04-08\n",
       "18      1986-04-09\n",
       "19      1986-04-10\n",
       "20      1986-04-11\n",
       "21      1986-04-14\n",
       "22      1986-04-15\n",
       "23      1986-04-16\n",
       "24      1986-04-17\n",
       "25      1986-04-18\n",
       "26      1986-04-21\n",
       "27      1986-04-22\n",
       "28      1986-04-23\n",
       "29      1986-04-24\n",
       "           ...    \n",
       "7953    2017-10-02\n",
       "7954    2017-10-03\n",
       "7955    2017-10-04\n",
       "7956    2017-10-05\n",
       "7957    2017-10-06\n",
       "7958    2017-10-09\n",
       "7959    2017-10-10\n",
       "7960    2017-10-11\n",
       "7961    2017-10-12\n",
       "7962    2017-10-13\n",
       "7963    2017-10-16\n",
       "7964    2017-10-17\n",
       "7965    2017-10-18\n",
       "7966    2017-10-19\n",
       "7967    2017-10-20\n",
       "7968    2017-10-23\n",
       "7969    2017-10-24\n",
       "7970    2017-10-25\n",
       "7971    2017-10-26\n",
       "7972    2017-10-27\n",
       "7973    2017-10-30\n",
       "7974    2017-10-31\n",
       "7975    2017-11-01\n",
       "7976    2017-11-02\n",
       "7977    2017-11-03\n",
       "7978    2017-11-06\n",
       "7979    2017-11-07\n",
       "7980    2017-11-08\n",
       "7981    2017-11-09\n",
       "7982    2017-11-10\n",
       "Name: Date, Length: 7983, dtype: object"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Date\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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